Data from: Integrated population models: bias and inference

Integrated population models (hereafter, IPMs) have become increasingly popular for the modeling of populations, as investigators seek to combine survey and demographic data to understand processes governing population dynamics. These models are particularly useful for identifying and exploring knowledge gaps within datasets, because they allow investigators to estimate biologically meaningful parameters, such as immigration and reproduction, that are uninformed by data. As IPMs have been developed relatively recently, model behavior remains relatively poorly understood. Much attention has been paid to parameter behavior such as parameter estimates near boundaries, as well as the consequences of dependent datasets. However, the movement of bias among parameters remains underexamined, particularly when models include parameters that are estimated without data. 2. To examine distribution of bias among model parameters, we simulated stable populations closed to immigration and emigration. We simulated two scenarios that might induce bias into survival estimates: marker induced bias in the capture-mark-recapture data, and heterogeneity in the mortality process. We subsequently ran appropriate capture-mark-recapture, state-space, and fecundity models, as well as integrated population models. 3. Simulation results suggest that when sampling bias exists in datasets, parameters that are not informed by data are extremely susceptible to bias. For example, in the presence of marker effects on survival of 0.1, estimates of immigration rate from an integrated population model were biased high (0.09). When heterogeneity in the mortality process was simulated, inducing bias in estimates of adult (-0.04) and juvenile (-0.097) survival rates, estimates of fecundity were biased by 46.2%. 4. We believe our results have important implications for biological inference when using integrated population models, as well as future model development and implementation. Specifically, parameters that are estimated without data absorb ~90% of the bias in integrated modelling frameworks. We suggest that investigators interpret posterior distributions of these parameters as a combination of biological process and systematic bias.